# from sklearn.naive_bayes import MultinomialNB # from sklearn.preprocessing import OneHotEncoder # X = xigua.iloc[:,:-1] # y = xigua.iloc[:,-1] # onehot = OneHotEncoder() # X = onehot.fit_transform(X) # y = np.asarray(y).reshape(-1, 1) # clf = MultinomialNB() # clf.fit(X, y) # print("*********") # print(clf.score(X, y)) #gaussian naive bayes test import pandas as pd from naive_bayes import GaussianNB gender = pd.read_csv('Gender_classification.csv', header=0, encoding='utf-8') test_sample = gender.iloc[-1, 1:] gender_droped = gender.drop(gender.shape[0] - 1, axis=0, inplace=False) Xtrain = gender_droped.iloc[:, 1:] ytrain = gender_droped.人 clf = GaussianNB() # print(clf.normal_density(x = 0, mu = 0, sigma = 1)) clf.fit(Xtrain, ytrain) # print(clf.predict_single_instance(test_sample)) print(clf.predict(Xtrain)) print("the accuracy of training data:", clf.score(Xtrain, ytrain)) # from scipy.stats import norm # print(norm.pdf(0 , loc = 0, scale = 1))